Abstract
A machine vision algorithm to recognize eggplant fruit and estimate its size for selective robotic harvesting was developed and tested. Images acquired in both outdoor and indoor conditions were examined. The algorithm was designed for a two-step operation. The first step was a simple color segmentation to distinguish low intensity for speeding and the second step was a vertical-dividing operation using two templates to eliminate noise such as stems and leaves. The vertical-dividing operation could exclude 60-pixels-length object and cope with the fruit angle up to approximately 20°.The algorithm could recognize eggplant fruits accurately at a high ratio from 80.0 to 97.5% with the operating time of approximately l sec. The fruit length and the fruit maximal diameter, which would be indices of selective harvesting, were estimated from geometric features of the fruit binary image. Estimation accuracy of the fruit maximal diameter was varying from 16.0 to 21.5% and higher than the one of the fruit length.
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Claims 6
Claim · model-written summary
The vertical-dividing operation excluded objects 60 pixels long.
- Taxon
- Eggplant
Solanum melongenat0 - Outcome
- exclude 60-pixels-length objecte0
- Value
- 60-pixelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“The vertical-dividing operation could exclude 60-pixels-length object”
Claim · model-written summary
The vertical-dividing operation handled fruit angles up to about 20 degrees.
- Taxon
- EggplantSolanum melongenat0
- Outcome
- fruit anglee0
- Value
- approximately 20°e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“cope with the fruit angle up to approximately 20°”
Claim · model-written summary
Eggplant fruit recognition rate ranged from 80.0 to 97.5%.
- Taxon
- eggplantSolanum melongenae0
- Outcome
- recognize eggplant fruits accuratelye0
- Value
- 80.0 to 97.5%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“recognize eggplant fruits accurately at a high ratio from 80.0 to 97.5%”
Claim · model-written summary
Algorithm operating time was approximately one second.
- Taxon
- EggplantSolanum melongenat0
- Outcome
- operating timee0
- Value
- approximately l sece0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“operating time of approximately l sec”
Claim · model-written summary
Fruit length and maximal diameter were estimated from binary-image geometry.
- Taxon
- EggplantSolanum melongenat0
- Outcome
- fruit length and the fruit maximal diametere0
- Comparator
- geometric features of the fruit binary imagee0
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“The fruit length and the fruit maximal diameter, which would be indices of selective harvesting, were estimated from geometric features of the fruit binary image”
Claim · model-written summary
Maximal diameter estimation accuracy ranged from 16.0 to 21.5% and exceeded fruit-length estimation accuracy.
- Taxon
- EggplantSolanum melongenat0
- Outcome
- Estimation accuracy of the fruit maximal diametere0
- Direction
- highere0
- Value
- 16.0 to 21.5%e0
- Comparator
- fruit lengthe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Eggplant”
e0“Estimation accuracy of the fruit maximal diameter was varying from 16.0 to 21.5% and higher than the one of the fruit length”